This work proposes an adaptive controller applied to servo systems. Its salient features are the use of the acceleration and the velocity of the desired trajectory for building the regressor vector, which is employed in a update law endowed with the classic e-modification, and the proposal of a new smooth bounding technique for limiting the values of the parameter estimates produced by the update law. The performance of the proposed adaptive controller is evaluated through experiments on a low-cost laboratory prototype. In addition, the experiments allow concluding that the proposed parameter bounding technique combined with the use of noise-free signals in the update law produce good closed-loop performance.

This article presents the optimal tuning of an Active Disturbance Rejection Controller (ADRC) applied to the tracking control of a servo system. The ADRC consists of a Luenberger Observer coupled with a Disturbance Observer. Its purpose is to reject the disturbances affecting the servo system and to impose a desired closed-loop dynamics. Previous results on this controller focus only on its stability analysis. Moreover, finding the controller parameters that provide optimal performance is difficult. For the foregoing reasons, this work proposes using the Particle Swarm Optimization (PSO) algorithm to tune the parameters of the ADRC. The restrictions imposed on the particles are obtained from the stability analysis of the ADRC. This allows discarding those solutions leading to closed-loop instability. Therefore, the algorithm delivers solutions where a fitness function is minimized, and the closed-loop system is stable. Finally, realtime experiments on a laboratory prototype show the performance of the proposed tuning method.